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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import uproot\n", "import numpy as np\n", "import sys\n", "import os\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import mplhep\n", "from mpl_toolkits import mplot3d\n", "import itertools\n", "import awkward as ak\n", "from scipy.optimize import curve_fit\n", "from utils.components import unique_name_ext_re\n", "mplhep.style.use([\"LHCbTex2\"])\n", "plt.rcParams[\"savefig.dpi\"] = 600\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "file = uproot.open(\n", " \"/work/cetin/LHCb/reco_tuner/data/resolutions_and_effs_B_thesis.root:Track/MatchTrackChecker_8319528f/Match;1\",\n", ")\n", "\n", "P_recoed = file[\"01_long_P_reconstructed;1\"].to_numpy()\n", "P_recoable = file[\"01_long_P_reconstructible;1\"].to_numpy()\n", "\n", "Pt_recoed = file[\"01_long_Pt_reconstructed;1\"].to_numpy()\n", "Pt_recoable = file[\"01_long_Pt_reconstructible;1\"].to_numpy()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "control eff: 0.8626619913200968\n", "new eff: 0.8626619913200968\n", "control eff: 0.8629752409817771\n", "new eff: 0.8629752409817771\n" ] } ], "source": [ "P_Velo_recoed = file[\"01_long_EndVelo_P_reconstructed;1\"].to_numpy()\n", "P_Velo_recoable = file[\"01_long_EndVelo_P_reconstructible;1\"].to_numpy()\n", "\n", "print(\"control eff: \", np.sum(P_recoed[0]) / np.sum(P_recoable[0]))\n", "print(\"new eff: \", np.sum(P_Velo_recoed[0]) / np.sum(P_Velo_recoable[0]))\n", "\n", "Pt_Velo_recoed = file[\"01_long_EndVelo_Pt_reconstructed;1\"].to_numpy()\n", "Pt_Velo_recoable = file[\"01_long_EndVelo_Pt_reconstructible;1\"].to_numpy()\n", "\n", "print(\"control eff: \", np.sum(Pt_recoed[0]) / np.sum(Pt_recoable[0]))\n", "print(\"new eff: \", np.sum(Pt_Velo_recoed[0]) / np.sum(Pt_Velo_recoable[0]))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0., 39546., 175333., 205172., 179814., 156192., 131918.,\n", " 111561., 96269., 82605., 72104., 62481., 54880., 48023.,\n", " 43043., 37810., 34533., 31242., 27997., 25422., 23137.,\n", " 20994., 19297., 17662., 16196., 14804., 13679., 12636.,\n", " 11687., 10738., 10125., 9329., 8681., 8080., 7424.,\n", " 6950., 6416., 6048., 5771., 5304., 4963., 4611.,\n", " 4379., 4095., 3844., 3512., 3303., 3104., 3020.,\n", " 2839., 2717., 2549., 2297., 2287., 2076., 2030.,\n", " 1875., 1791., 1684., 1557., 1559., 1418., 1389.,\n", " 1321., 1245., 1164., 1122., 1055., 1008., 961.,\n", " 920., 899., 833., 839., 746., 744., 725.,\n", " 656., 657., 673., 601., 547., 552., 504.,\n", " 524., 452., 440., 427., 438., 395., 412.,\n", " 408., 392., 349., 328., 328., 289., 308.,\n", " 271., 297.]),\n", " array([ 0., 1000., 2000., 3000., 4000., 5000., 6000.,\n", " 7000., 8000., 9000., 10000., 11000., 12000., 13000.,\n", " 14000., 15000., 16000., 17000., 18000., 19000., 20000.,\n", " 21000., 22000., 23000., 24000., 25000., 26000., 27000.,\n", " 28000., 29000., 30000., 31000., 32000., 33000., 34000.,\n", " 35000., 36000., 37000., 38000., 39000., 40000., 41000.,\n", " 42000., 43000., 44000., 45000., 46000., 47000., 48000.,\n", " 49000., 50000., 51000., 52000., 53000., 54000., 55000.,\n", " 56000., 57000., 58000., 59000., 60000., 61000., 62000.,\n", " 63000., 64000., 65000., 66000., 67000., 68000., 69000.,\n", " 70000., 71000., 72000., 73000., 74000., 75000., 76000.,\n", " 77000., 78000., 79000., 80000., 81000., 82000., 83000.,\n", " 84000., 85000., 86000., 87000., 88000., 89000., 90000.,\n", " 91000., 92000., 93000., 94000., 95000., 96000., 97000.,\n", " 98000., 99000., 100000.]))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "P_recoed" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tuner", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.1.0" } }, "nbformat": 4, "nbformat_minor": 2}
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